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Based on the mutual compensation of offshore wind energy and wave energy, a hybrid wind–wave power generation system can provide a highly cost-effective solution to the increasing demands for offshore power. To provide comprehensive guidance for future research, this study reviews the energy conversion and coupling technologies of existing hybrid Wind–wave power generation systems which have not been reported in previous publications. The working principles of various wind and wave energy conversion technologies are summarised in detail. In addition, existing energy coupling technologies are specifically classified and described. All aforementioned technologies are comprehensively compared and discussed. Technological gaps are highlighted, and future development forecasts are proposed. It is found that the integration of hydraulic wind turbines and oscillating wave energy converters is the most promising choice for hybrid wind–wave power extraction. DC and hydraulic coupling are expected to become mainstream energy coupling schemes in the future. Currently, the main technological gaps include short their operating life, low energy production, limited economic viability, and the scarcity of theoretical research and experimental tests. The field offers significant opportunities for expansion and innovation. [ABSTRACT FROM AUTHOR]

Among renewable energy sources, wind energy is regarded as one of the fastest-growing segments, which plays a key role in enhancing environmental quality. Wind turbines are generally located in remote and harsh environments. Bearings are a crucial component in wind turbines, and their failure is one of the most frequent reasons for system breakdown. Wind turbine bearing faults are usually very localized during their early stages which is precisely when they need to be detected. Hence, the early diagnosis of bearing faults holds paramount practical significance. In order to solve the problem of weak pulses being masked by noise in early failure signals of rolling element bearings, a novel fault diagnosis method is proposed based on the combination of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and an improved TFR demodulation method. Initially, the decomposition of vibration signals using CEEMDAN is carried out to obtain several intrinsic mode functions (IMFs). Subsequently, a novel KC indicator that combines kurtosis and the correlation function is designed to select the effective components for signal reconstruction. Finally, an innovative approach based on the continuous wavelet transform (CWT) for multi-scale demodulation analysis in the domain of time–frequency representation (TFR) is also introduced to extract the envelope spectrum. Further fault diagnosis can be achieved by the identification of the fault characteristic frequency (FCF). This study focuses on the theoretical exploration of bearing faults diagnosis algorithms, employing modeling and simulation techniques. The effectiveness and feasibility of the proposed method are validated through the analysis of simulated signals and experimental signals provided by the Center for Intelligent Maintenance Systems (IMS) of the University of Cincinnati and the Case Western Reserve University (CWRU) Bearing Data Center. The method demonstrates the capability to identify various types of bearing faults, including outer race and inner race faults, with a high degree of computational efficiency. Comparative analysis indicates a significant enhancement in fault diagnostic performance when compared to existing methods. This research contributes to the advancement of effective bearing fault diagnosis methodologies for wind turbines, thereby ensuring their reliable operation. [ABSTRACT FROM AUTHOR]

The marine ranching industry in China is transitioning from traditional farming to a digital and intelligent model. The use of new technologies, algorithms, and models in the era of artificial intelligence (AI) is a key focus to enhance the efficiency, sustainability, and resilience of marine ranch operations, particularly in risk and disaster management. This study proposes a methodology for applying deep reinforcement learning to decision making in this domain. The approach involves creating an environmental model based on decision objects and scenarios, determining the number of decision makers, and selecting a single or multi-agent reinforcement learning algorithm to optimize decision making in response to randomly generated disasters. Three core innovations are presented: the development of a disaster simulator for marine ranching scenarios, the application of reinforcement learning algorithms to address risk and disaster management problems in marine ranching. Future research could focus on further refining the methodology by integrating different data sources and sensors and evaluating the social and economic impacts of AI-driven marine ranching. Overall, this study provides a foundation for further research in this area, which is expected to play an increasingly important role in global food production, environmental sustainability, and energy efficiency. [ABSTRACT FROM AUTHOR]

The safety of power transmission systems in wind turbines is crucial to the wind turbine's stable operation and has attracted a great deal of attention in condition monitoring of wind farms. Many different intelligent condition monitoring schemes have been developed to detect the occurrence of defects via supervisory control and data acquisition (SCADA) data, which is the most commonly applied condition monitoring system in wind turbines. Normally, artificial neural networks are applied to establish prediction models of the wind turbine condition monitoring. In this paper, an alternative and cost-effective methodology has been proposed, based on the group method of data handling (GMDH) neural network. GMDH is a kind of computer-based mathematical modelling and structural identification algorithm. GMDH neural networks can automatically organize neural network architecture by heuristic self-organization methods and determine structural parameters, such as the number of layers, the number of neurons in hidden layers, and useful input variables. Furthermore, GMDH neural network can avoid over-fitting problems, which is a ubiquitous problem in artificial neural networks. The effectiveness and performance of the proposed method are validated in the case studies. [ABSTRACT FROM AUTHOR]

This paper presents a constant-pressure hydraulic PTO system that can convert stored pressure energy into electrical energy at a stable speed through hydraulic motors and generators. A multi-chamber cylinder can be connected to the main power generation circuit by check valves, and the motor displacement can be controlled by a fuzzy controller to maintain the main power generation circuit under stable pressure. The hydraulic transformer can control the forces applied to the floater. The hydrodynamic parameters of the floater are calculated by AQWA, and the optimal PTO damping of the hydraulic system is analyzed as the target of transformer control. MATLAB/Simulink and AMESim are used to carry out the co-simulation. Three kinds of wave elevation time-series for the specific state are designed for the simulation. In the co-simulation, three approaches are carried out for the simulation including no control strategy, fuzzy control with a fixed transformer ratio, and fuzzy control with a variable transformer ratio. Under the fuzzy control with a fixed transformer ratio, the floater displacement and captured energy do not increase significantly, but the oil pressure fluctuation is very stable, which indicates that the fuzzy controller maintains the stability of the main power circuit. While under fuzzy control with a variable transformer ratio, the power generation is not larger than those under no control strategy or fuzzy control with a fixed transformer ratio, which proves that this hydraulic transformer concept is less efficient. [ABSTRACT FROM AUTHOR]